Starting point

What to want

Don't break it!

Clinical Informatics: information model

Clinical Informatics: terminology model

Clinical Informatics: connected models

expressivity overlap

Ideally, the terminology and information models are complementary:

Some relationships found in both models:

• evidenced by
• severity
• composition (part-of)
• aggregation

These contribute to a "TermInfo problem".

(intersection of rim ActRelationship typeCodes with SNOMED's context and event concepts.)

If it's all RDF, it's easy to combine and normalize.

What's needed for clinical practice

• drug lists
• allergies
• immunizations
• vitals
• encounters
• procedures
• labs
• problem statements
• diagnosis
• treatment plans
• outcomes
• genomics

What's needed for research over populations

Representation

protocol specifies a small number of specific acquisitions with special terminology codes.

Data scale

Growing data size:

• imaging: PET scans, CT scans, FMRI
• molecular diagnostics
• genome (3 billion base pairs)
• biome (300 billion base pairs)

Growing data vectors:

• next-generation (post-Sanger) sequencing. (name stuck to Moores law?)
• online genomics services: Navigenics, 23andMe and Helix Health
• online support communities: CureTogether, patientslikeme
• virtual visits: American Well, Cisco HealthPresence, RelayHealth, Teladoc, Medfusion Virtual Office
• remote clinical devices: RP-7i (robo-nurse), iPill
• commodity clinics: minute clinic (CVS), take care clinic (Walgreens), RediClinic (H-E-B), Target Clinic, The Little Clinic
• consumer devices: asthma-charter, USB chip for STDs, glucose meter, urinalysis

Policy

Clinical care

• US: Afordable Care Act mandates patient portability in the form of HL7's C-CDA standard.
• Europe: Innovative Medicines Initiative focusing on clinical data re-use
  • EHR4CR: Electronic Health Records for Clinical Research
  • SALUS: Post-market patient safety

Clinical trials

• The European Medicines Agency (EMA) pushing publication of clinical-trial data for public health,
  Goal of understandable clinical data formats.
• FDA goal of improved use of submitted clinical trial data through Therapeutic Areas.
• Pharmaceutical companies have attempted to use submission standards to create study repositories.

How's that going?

Clinical care

Too early to tell.

263870008 (SNOMED code for Sequestration)

Clinical trials

Around 2009, CDISC members identified a need for intra and inter pharma sharing of trial metadata:

• questions
• value sets

They were going to call it SHARE.

4 years later,

• GSK started their own.
• CDISC2RDF Roche, AZ mapping SDTM to RDF
  rolled into PhUSE Emerging Technologies.
• Abbvie, Boehringer Ingelheim, AstraZeneca, BMS, GSK, J&J, Lilly, Pfizer, Roche, Sanofi formed TranCelerate.

Three forms of standards:

• syntactic standards:
  fax-level interoperability shares data between humans.
  HTML's ubiquitous rendering is a great success.
• semantic standards:
  code systems, data types and value sets.
  human eyes not required.
• Semantic Web-based standards:
  use RDF, OWL, Web architecture etc. in definitions and data.
  leverage growing body of standards and expertise.

Semantic standards:

Semantic Web-based standards:

Simplify the standards process.

• Don't invent a graph language.
• Don't invent app-specific logic.
• Don't implement app-specific logic
  and hope others do the same.
• Don't wait 'till you've invented everything.
• Don't invent distributed extensibility.
• Don't invent anything if you can help it.

RDF-y Schemas/Ontologies

What to re-use:

• BRIDG as OWL
• 21090 via CDISC2RDF
• 11179 via CDISC2RDF
• SNOMED downloadable from IHTSDO or via CTS2
• LOINC via ADEpedia
• RIM as O-RIM -- grounds BRIDG, C-CDA (to use clinical data for clinical research)

What to expect from semantic interop

• cheaper
  less redundancy, more preventative interventions, better-informed policy choices
• faster
  artifact of reducing redundancy
• better
  • interaction monitoring
  • adverse event detection
  • statistical diagnostic support
• data available to consumer
  democratize apps (à la SMART Platforms) and sharing (à la Consent to Research)
• connected to more inputs
• performance metrics
• tailored/translational therapeutics
  connection to growing body of life sciences knowledge.
• plan implementation and conformance
• benevolent robotic overlords

Population wisdom

Bedside to Bench and Back and Beyond...

• population-based conclusions
  from individual data instances
• individual treatment options
  from population experience
• common data appearing in multiple contexts
  e.g. syndromic surveillance
• outcomes associated with
  • procedures
  • interventions
  • substance administrations

What to expect from SemWeb standards

• plumbing

Plumbing opportunities

• Term simplification.
• Minimalist term evolution.
• Taxonomy supplements.
• Instance identification
  e.g patients, prescriptions, ….
• Trivial extensibility.

Term simplification

PREFIX snomed: <http://ihtsdo.org…20130131…> 
PREFIX hl7: <http://hl7.org/owl/metadata#> 

<myObs> hl7:coding snomed:282291009 .

Term oversimplification?

• mixed versions?
• version evolution?

Minimalist term evolution

• Version terms, not terminologies.
• Annotate with govornance metadata.
• Marginally increased ontologist burden.
• Hugely decreased consumer burden.

Taxonomy supplements

Biggest challenges

• 5 outreach/education.
• 4 architecture.
• 3 validation.
• 2 knitting/gluing.
• 1 physician interface.

Thank you

Come geek with us in the W3C Semantic Web in Health Care and Life Sciences IG